The automated extraction of features from magnetic resonance images (MRI) of the brain is an increasingly important process in neuroimaging. Since the brain anatomy varies significantly across subjects and can undergo significant change, either during aging or through disease progression, finding an appropriate way of dealing with anatomical differences during feature extraction has gained increasing attention in recent years.
Amongst the most popular methods for dealing with this variability are atlas-based approaches. In the context of the present work, an “atlas” is a dataset (which may be a 3D image, a 2D image, images of any dimension, or a set of images) having annotations or labels in order to identify points, regions or structures within the image.
Atlas-based approaches assume that the atlases can encode the anatomical variability either in a probabilistic or statistical fashion. When building representative atlases, it is important to register all images to a template that is unbiased towards any particular subgroup of the population. Two approaches using the large deformation diffeomorphic setting for shape averaging and atlas construction have been proposed by Avants and Gee (2004) and Joshi et al. (2004). Template-free methods for co-registering images form an established framework for spatial image normalization. In a departure from approaches that seek a single representative average atlas, two more recent methods describe ways of identifying the modes of different populations in an image dataset (Blezek and Miller, 2007; Sabuncu et al., 2008).
To design variable atlases dependent on subject information, a variety of approaches have been applied in recent years to the problem of characterizing anatomical changes in brain shape over time and during disease progression. Davis et al. (2007) describe a method for population shape regression in which kernel regression is adapted to the manifold of diffeomorphisms and is used to obtain an age-dependent atlas. Ericsson et al. (2008) propose a method for the construction of a patient-specific atlas where different average brain atlases are built in a small deformation setting according to meta-information such as sex, age, or clinical factors.
Methods for extracting features or biomarkers from magnetic resonance (MR) brain image data often begin by automatically segmenting regions of interest. A very popular segmentation method is to use label propagation which transforms labels from an atlas image to an unseen target image by bringing both images into alignment. Atlases are typically, but not necessarily, manually labelled. Early work using this approach was proposed by Bajcsy et al. (1983) as well as more recently Gee et al. (1993) and Collins et al. (1995). The accuracy of label propagation strongly depends on the accuracy of the underlying image alignment. To overcome the reliance on a single segmentation, Warfield et al. (2004) proposed STAPLE, a method that computes for a collection of segmentations a probabilistic estimate of the true segmentation. Rohlfing et al. (2004) demonstrated the improved robustness and accuracy of a multi-classifier framework where the labels propagated from multiple atlases are combined in a classifier fusion step to obtain a final segmentation of the target image. Label propagation in combination with classifier fusion was successfully used to segment a large number of structures in brain MR images by Heckemann et al. (2006).
Due to the wide range of anatomical variation, the selection of atlases becomes an important issue in multi-atlas segmentation. The selection of suitable atlases for a given target helps to ensure that the atlas-target registrations and the subsequent segmentation are as accurate as possible. Wu et al. (2007) describe different methods for improving segmentation results in the single atlas case by incorporating atlas selection. Aljabar et al. (2009) investigate different similarity measures for optimal atlas selection during multi-atlas segmentation. Van Rikxoort et al. (2008) propose a method where atlas combination is carried out separately in different sub-windows of an image until a convergence criterion is met. These approaches show that it is meaningful to select suitable atlases for each target image individually. Although an increasing number of MR brain images are available, the generation of high-quality manual atlases is a labour-intensive and expensive task (see e.g. Hammers et al. (2003)). This means that atlases are often relatively limited in number and, in most cases, restricted to a particular population (e.g. young, healthy subjects). This can limit the applicability of the atlas database even if a selection approach is used. To overcome this, Tang et al. (2009) seek to produce a variety of atlas images by utilizing a PCA model of deformations learned from transformations between a single template image and training images. Potential atlases are generated by transforming the initial template with a number of transformations sampled from the model. The assumption is that, by finding a suitable atlas for an unseen image, a fast and accurate registration to this template may be readily obtained. Test data with a greater level of variation than the training data would, however, represent a significant challenge to this approach. Additionally, the use of a highly variable training dataset may lead to an unrepresentative PCA model as the likelihood of registration errors between the diverse images and the single template is increased. This restriction makes this approach only applicable in cases where a good registration from all training images to the single initial template can be easily obtained.
Atlas-based segmentation benefits from the selection of atlases similar to the target image (Wu et al., 2007; Aljabar et al., 2009). However, in practice, the initial atlases may only represent a specific subgroup of the target image population.
There is therefore a desire to be able to propagate a relatively small number of atlases through to a large and diverse set of MR brain images exhibiting a significant amount of anatomical variability.
Prior work where automatically labelled brain images were used to label unseen images did not result in an improvement of segmentation accuracy over direct multi-atlas propagation. In (Heckemann et al., 2006), when multiple relatively homogenous atlases were propagated to randomly selected intermediate images that were used as single atlases for the segmentation of unseen images, the resulting average Dice overlaps with manual delineations were 0:80, compared with 0:84 for direct multi-atlas propagation and fusion. In a second experiment, single atlases were propagated to randomly selected intermediate subjects that were then further used for multi-atlas segmentation, resulting in Dice overlaps with manual delineations of 0:78 at best.
Further background art is provided by US 2007/0053589 A1, US 2008/0154118 A1 and WO 2009/093146 A1, all of which disclose methods for segmenting image data.